"There's often a gap between what you can measure and what
really matters," Christian tells Tech Insider. "And there's a
tendency to over-optimize for the thing that you can measure for
the thing that really matters."

That over-optimization is what statisticians call "overfitting,"
and it appears again and again when researchers want to build
models for their data. In being thorough, they end up generating
a lot of noise that makes it much harder to find the signal.

Diet fads are prime examples of overfitting because they turn an
ongoing process — healthy eating — into an over-simplified
mandate.

Typically, fads emerge when a single study finds a food or action
helps a small group of people. This is what Christian means by
"the thing that you can measure." The study has data that shows,
however modestly, that coffee lowers blood pressure or nuts lower
cholesterol.

That modest finding then gets blown out of proportion, typically
through news media, and gets circulated around as gospel. People
start going on juice cleanses and detox diets regardless of
whether it makes sense for them.

The authors urge people to resist their tendency to make abrupt
and "high-amplitude" changes to their habits. "Just because
there's one study that suggests X, Y, or Z, that can be really
tempting to overfit the most recent piece of information,"
Christian says. But it could easily be the case that six months
from now, those same researchers find reasons to make a
recommendation against X, Y, or Z.

In essence, people overfit the dietary advice because they start
prioritizing the specific food over the process of eating healthy
— similar to how people may exercise to an unhealthy degree to
look better instead of trying to be healthier.

Christian and Griffiths call this the "idolatry of data."

"Overfitting the signals — adopting an extreme diet to lower body
fat and taking steroids to build muscle, perhaps — can make you a
picture of good health," they write, "but only the picture."

So do your best to avoid most dietary advice you hear in the
news. A lot of it is poorly supported by data.